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HP HPE2-N69 考試大綱：

主題	簡介
主題 1	Explain how the Machine Learning Development Environment uses resources and schedules workloads Understand the challenges customers face in training DL models
主題 2	Describe the HPE Machine Learning Development Environment software architecture and deployment options Have a conversation with customers about machine learning (ML) and deep learning (DL)
主題 3	Qualify customers for HPE Machine Learning Development Environment and System Articulate the business case for HPE Machine Learning Development solutions
主題 4	Explain how HPE Machine Learning Development Environment helps customers surmount their challenges Run a proof of concept (PoC)
主題 5	Size HPE Machine Learning Development Environment and System solutions Understand machine learning (ML) and deep learning (DL) fundamentals

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HPE2-N69熱門考題，免費下載HPE2-N69考題

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最新的 HPE Product Certified - AI and Machine Learning HPE2-N69 免費考試真題 (Q34-Q39):

問題 #34
You are helping a customer start to implement hyper parameter optimization (HPO) with HPE Machine learning Development Environment. An ML engineer is putting together an experiment config file with the desired Adaptive A5HA settings. The engineer asks you questions, such as how many trials will be trained on the max length and what the min length for all trials will be.
What should you explain?

• A. The engineer should access the HPE Machine Learning Development online calculator and input the mode, maxtrials, maxlength, divisor, and max_runs.
• B. The engineer should run a preliminary experiment with one tenth the desired number of max trials, assess the results, and then run the full experiment.
• C. The engineer should upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.
• D. The engineer should run the "det preview-search" command, referencing the experiment config.

答案：B
問題 #35
What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?

• A. It automatically cleans up data to create better end results.
• B. It uses a centralized training architecture that is highly efficient.
• C. It helps DL projects complete faster for a faster ROI.
• D. It helps companies deploy models and generate revenue.

答案：C
問題 #36
A customer has Men expanding its deep learning (DO prefects and is confronting several challenges. Which of these challenges does HPE Machine Learning Development Environment specifically address?

• A. Complex model deployment processes
• B. Complex and time-consuming hyperparameter optimization (HPO)
• C. Complex and time-consuming data cleansing process
• D. Time-consuming data collection

答案：B 解題說明：
The HPE Machine Learning Development Environment specifically addresses Complex and time-consuming hyperparameter optimization (HPO). HPO is a process used to identify the most effective set of hyperparameters for a given machine learning model. HPE's ML Development Environment provides a suite of tools that allow users to quickly and easily design and deploy deep learning models, as well as optimize their hyperparameters to get the best results.
問題 #37
What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?

• A. Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
• B. Slingshot
• C. InfiniBand
• D. Data Center Bridging (OCB)-enabled Ethernet

答案：A 解題說明：
HPE Machine Learning Development System uses Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE) for high-speed, agent-to-agent communications. This technology allows data to be transferred directly between agents without the need for copying, which results in improved performance and reduced latency.
問題 #38
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?

• A. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
• B. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
• C. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.
• D. Implementing HPO manually can be time-consuming and demand a great deal of expertise.

答案：B
問題 #39
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